&lt;title&gt;Similarity retrieval from image databases: neural networks can deliver&lt;/title&gt;

Rickman, R.; Stonham, T.J.

doi:10.1117/12.143658

Cited by 4 publications

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“…They employ a graph representation for storage and retrieval of drawings. Rickham and Stonham (1993) have proposed a neural network Pattern Recognition Letters 20 (1999) 109±118 based coding scheme for retrieval of images according to the degree of similarity with a query image. Hibler et al (1992) have proposed an augmented entity relationship model to overcome the limitations of traditional key word system.…”

Section: Introductionmentioning

confidence: 99%

Color and spatial feature for content-based image retrieval

Kankanhalli

Mehtre²,

Huang

1999

Pattern Recognition Letters

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Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify`keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very dicult task. In this paper, we present a content-based retrieval method which obviates the need to describe certain contents of an image to be archived and retrieved. The proposed method computes image features automatically from a given image and they can be used to archive and/or retrieve images. These features are based on color and its spatial distribution information in an image. We have also developed a similarity measure to compare the color and spatial feature similarity of two images. This technique has been developed and tested for content-based similarity retrieval of images on two databases consisting of: (i) 100 test images and (ii) 800 actual trademarks images. The experimental results show a high eciency of retrieval. Ó

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Section: Introductionmentioning

confidence: 99%

Color and spatial feature for content-based image retrieval

Kankanhalli

Mehtre²,

Huang

1999

Pattern Recognition Letters

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A self-processing network model for relational databases

De-Medonsa

Kraus

Shiftan

1999

IEEE Trans. Syst., Man, Cybern. B

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In this paper, a model which combines relational databases with self-processing networks is proposed in order to improve the performance of very large databases. The proposed model uses an approach which is radically different from all other distributed database models, where each computer processes a portion of the database. In the self-processing network model, the network structure which consists of nodes and connections, captures the data and the relationships by assigning them unique, connected control, and data nodes. The network activity is the mechanism that performs the relational algebra operations. No data transmission is needed, and since data nodes are common to all the relations, integrity and elimination of data redundancy are achieved. An extension of the model, by interconnecting the data nodes via weighted links, provides us with properties that are embedded in neural networks, such as fuzziness and learning.

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Content-Based Image Retrieval System Using Neural Networks

Ikeda

Hagiwara

2000

Int. J. Neur. Syst.

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An effective image retrieval system is developed based on the use of neural networks (NNs). It takes advantages of association ability of multilayer NNs as matching engines which calculate similarities between a user's drawn sketch and the stored images. The NNs memorize pixel information of every size-reduced image (thumbnail) in the learning phase. In the retrieval phase, pixel information of a user's drawn rough sketch is inputted to the learned NNs and they estimate the candidates. Thus the system can retrieve candidates quickly and correctly by utilizing the parallelism and association ability of NNs. In addition, the system has learning capability: it can automatically extract features of a user's drawn sketch during the retrieval phase and can store them as additional information to improve the performance. The software for querying, including efficient graphical user interfaces, has been implemented and tested. The effectiveness of the proposed system has been investigated through various experimental tests.

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<title>Similarity retrieval from image databases: neural networks can deliver</title>

Cited by 4 publications

References 0 publications

Color and spatial feature for content-based image retrieval

Color and spatial feature for content-based image retrieval

A self-processing network model for relational databases

Content-Based Image Retrieval System Using Neural Networks

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